Management of multidimensional discrete data

Spatial database management involves two main categories of data: vector and raster data. The former has received a lot of in-depth investigation; the latter still lacks a sound framework. Current DBMSs either regard raster data as pure byte sequences where the DBMS has no knowledge about the underlying semantics, or they do not complement array structures with storage mechanisms suitable for huge arrays, or they are designed as specialized systems with sophisticated imaging functionality, but no general database capabilities (e.g., a query language). Many types of array data will require database support in the future, notably 2-D images, audio data and general signal-time series (1-D), animations (3-D), static or time-variant voxel fields (3-D and 4-D), and the ISO/IEC PIKS (Programmer's Imaging Kernel System) BasicImage type (5-D). In this article, we propose a comprehensive support ofmultidimensional discrete data (MDD) in databases, including operations on arrays of arbitrary size over arbitrary data types. A set of requirements is developed, a small set of language constructs is proposed (based on a formal algebraic semantics), and a novel MDD architecture is outlined to provide the basis for efficient MDD query evaluation.     

A semantic modeling approach for image retrieval by content

We introduce a semantic data model to capture the hierarchical, spatial, temporal, and evolutionary semantics of images in pictorial databases. This model mimics the user's conceptual view of the image content, providing the framework and guidelines for preprocessing to extract image features. Based on the model constructs, a spatial evolutionary query language (SEQL), which provides direct image object manipulation capabilities, is presented. With semantic information captured in the model, spatial evolutionary queries are answered efficiently. Using an object-oriented platform, a prototype medical-image management system was implemented at UCLA to demonstrate the feasibility of the proposed approach.     

基于图像的计算机辅助数控编程软件研究

研究了基于图像的数控自动编程方法，包括影响自动编程、图像骨架自动编程和轮廓自动编程方法，并实现了使用该方法的计算机辅助数控编程软件。     

Image processing using cellular neural networks based on multi-valued and universal binary neurons

Multi-valued and universal binary neurons (MVN and UBN) are the neural processing elements with the complex-valued weights and high functionality. It is possible to implement an arbitrary mapping described by partially defined multiple-valued function on the single MVN. An arbitrary mapping described by partially defined or fully defined Boolean function, which can be non-threshold, may be implemented on the single UBN. The quickly converging learning algorithms exist for both types of neurons. Such features of the MVN and UBN may be used for solving the different problems. One of the most successful applications of the MVN and UBN is their usage as basic neurons in the Cellular Neural Networks (CNN). It opens the new effective opportunities in nonlinear image filtering and its applications to noise reduction, edge detection and solving of the super resolution problem. A number of experimental results are presented to illustrate the performance of the proposed algorithms.An erratum to this article can be found at     

城市形象的建筑环境美学设计问题

城市形象是创造富于个性化城市的关键,而建筑环境美学设计水准则是体现城市形象的要素之一。据此,作者从分析中国城市形象的美学误区出发,研究了不同城市建筑品位的形成特点,进而提出塑造好宜人城市美学空间的科学思考。     

用迭代法消除数字图像放大后的模糊

用迭代法对数字图像经过插值放大后产生的模糊问题进行了研究，把数字图像插值放大造成的模糊看成是点扩散函数与清晰图像卷积的结果，根据插值算法可以得到点扩散函数，由于数字图像解卷积是典型的解线性方程组的问题，用雅可比（Jacobi）迭代法得到了很好的结果。与频谱空间变换的方法相比，迭代法没有分母为0的问题和空间变换过程造成的舍入误差。     

灰度图像的子块压缩方法

从灰度图像的局部几何特性出发，把图像中像素灰度相近或相同的区域当作一个整体处理，从而达到图像压缩的目的。计算机仿真实验表明，该方法可以获得产高的压缩比和峰值信噪比，有效地消除方块效应，很好地保持目标细节，具有较高的实用价值。     

The role of occlusion in the perception of depth, lightness, and opacity.

A theory is presented that explains how the visual system infers the lightness, opacity, and depth of surfaces from stereoscopic images. It is shown that the polarity and magnitude of image contrast play distinct roles in surface perception, which can be captured by 2 principles of perceptual inference. First, a contrast depth asymmetry principle articulates how the visual system computes the ordinal depth and lightness relationships from the polarity of local, binocularly matched image contrast. Second, a global transmittance anchoring principle expresses how variations in contrast magnitudes are used to infer the presence of transparent surfaces. It is argued that these principles provide a unified explanation of how the visual system computes the 3-D surface structure of opaque and transparent surfaces. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

An improved analysis of experimental data from 125 I hot-line autoradiographs: allowing for the effects of background grains

In this paper we suggest a new statistical method of correcting the results of hot-line experiments for the effects of background sources and we use the new method to reassess the adequacy of three probability distributions proposed in the literature for image spread from line sources. The data are from sources labelled with ¹²⁵I in semi-thin resin sections 0·4-0·8 μm in thickness. The new method reveals that two of the models describe the empirical distributions more closely than earlier analysis had suggested, and it confirms an increasing relationship between half distance of image spread and the thickness of the source. However, it also confirms that considerable ‘inter hot-line’ experimental variation remains, even after background correction. This suggests that multiple experiments are needed to produce reliable estimates of half distance.     

A VLSI architecture for a half-edge-based corner detector

Corner detection is a low-level feature detection operator that is of great use in image processing applications, for example, optical flow and structure from motion by image correspondence. The detection of corners is a computationally intensive operation. Past implementations of corner detection techniques have been restricted to software. In this paper we propose an efficient very large-scale integration (VLSI) architecture for detection of corners in images. The corner detection technique is based on the half-edge concept and the first directional derivative of Gaussian. Apart from the location of the corner points, the algorithm also computes the corner orientation and the corner angle and outputs the edge map of the image. The symmetrical properties of the masks are utilized to reduce the number of convolutions effectively, from eight to two. Therefore, the number of multiplications required per pixel is reduced from 1800 to 392. Thus, the proposed architecture yields a speed-up factor of 4.6 over conventional convolution architectures. The architecture uses the principles of pipelining and parallelism and can be implemented in VLSI.